Where in London is manufacturing taking place?
The type of data we have explored offers different ways to answer this question however there are some limitations. The 4 main data sets have the following general characteristics:
- Directory of London Businesses (SIC) — Point data of ‘financial accounts submissions’
- BRES (employment survey) — Number of jobs per LSOA by Industry (SIC)
- IDBR (business register) — Number of local units per MSOA by Industry (SIC)
- Industrial Land Designations — GLA polygons of Strategic Industrial land (SIL), Locally Significant Industrial Site (LSIS) and NALs (non-designated industrial areas)
Notes:
SIC is ‘Standard Industrial Classification’
A more detailed descritpion of 1, 2, 3 can be seen here https://npalomin.github.io/pnum/MPL.html and 4 here https://npalomin.github.io/pnum/GLA_indLand/GLA_indLand.html
Despite the fact that 1, 2, and 3 represent the same theme; ‘Manufacturing’ according to SIC, the spatial resolution and geometric representation of the data (varying size areas and points) poses challenges for data integration or comparaison.
Clustered vs dispersed?
Kernel density estimation (KDE) may provide an exploratory tool for hot-spot and cool-spot analysis of SIC point data. However this kind analysis is only exploratory (variations in density will show different results)
More computationally intensive techniques include the analysis of SIC point data (discrete observations) with point pattern analysis to quantify the spatial pattern (Quadrat Analysis, Ripley’s K and DBSCAN)
These last methods can also be used with area data (OAs) to check if manufacturing areas cluster together accross London. Nevertheless, perhaps a visualisation of the point data is sufficient.
In and around high streets or major street networks?
Space Syntax data can be overlaid on top of Manufacturing POINTS, JOBS per LSOA, and LOCAL UNITS per MSOA
On designated industrial states? (segragated)
How can these be exactly identified? This ‘atribute’ doesn’t seem to be included in the GLA data set.
In mostly mixed use environments? (integrated)
Space Synatax data could be used as a proxy
Along water networks? (i.e. historical path dependency)
This data set has not yet been searched.
East/west/north/south?
This could be answered with the point data visualization.
Outer vs inner London?
This data set can be easily obtained.
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